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source("tianfengRwrappers.R")
ds2 <- readRDS("ds2.rds")
selected_features <- read.csv("./datatable/ds2_features.csv", stringsAsFactors = F)
selected_features <- selected_features$Feature
Idents(ds2) <- ds2$seurat_clusters
ggobj <- multi_featureplot(selected_features[1:9], ds2, labels = NULL)
ggsave("ds2_features.png", device = png, height = 8, width = 8, plot = ggobj)

umap plot

ggsave("./fig2/ds2_ACumap.png", device = png, height = 4, width = 6, 
       plot = umapplot(ds2_AC, group.by = "Classification1", label.size = 5))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

ggsave("./fig2/ds2_PAumap.png", device = png, height = 4, width = 6, 
       plot = umapplot(ds2_PA, group.by = "Classification1", label.size = 5))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

Where is the Fibromyocyte

LUM

sep = 0.3
surfaceplot2("LUM",ds2,x_seq = seq(-2,13, sep), y_seq = seq(2,17,sep))

addmodulescore

geneset <- read.table("SMC")

ds2_AC <- AddModuleScore(ds2_AC,features = geneset, name = 'SMC_score')
ds2_PA <- AddModuleScore(ds2_PA,features = geneset, name = 'SMC_score')

(f("SMC_score1",label = F, ds2_AC) + scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_AC_SMCscore.png", device = png, height = 4, width = 5, plot = .)
(f("SMC_score1",label = F, ds2_PA) + scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_PA_SMCscore.png", device = png, height = 4, width = 5, plot = .)

# dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')

geneset <- read.table("FB")

ds2_AC <- AddModuleScore(ds2_AC,features = geneset, name = 'FB_score')
ds2_PA <- AddModuleScore(ds2_PA,features = geneset, name = 'FB_score')

(f("FB_score1", label = F, ds2_AC) +scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_AC_FBscore.png", device = png, height = 4, width = 5, plot = .)
(f("FB_score1", label = F, ds2_PA) +scale_colour_gradient(low="#1E90FF", high="#ff2121")) %>%
  ggsave("./fig2/ds2_PA_FBscore.png", device = png, height = 4, width = 5, plot = .)

key features have gradient expression

BGN LUM

ridgetheme <- theme(plot.title = element_text(size = 15,color="black",hjust = 0.5),
                 axis.title = element_text(size = 15,color ="black"), 
                 axis.text = element_text(size = 15,color = "black"),
                 panel.grid.minor.y = element_blank(),
                 panel.grid.minor.x = element_blank(),
                 axis.text.x = element_text(angle = 0, hjust = 1),
                 panel.grid=element_blank(),
                 legend.position = "top",
                 legend.text = element_text(size= 15),
                 legend.title= element_text(size= 15)) 

ridge plot

df <- FetchData(ds2_AC,vars = c("FB_score1","SMC_score1","BGN","LUM","UMAP_1","UMAP_2"))
# df <- arrange(df,FB_score1,by_group = F)
data <- cbind(df,index = 1:nrow(df),cluster = Idents(ds2_AC))

(ggplot(data,aes(x=SMC_score1)) + geom_point(aes(y = BGN, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + theme_classic() + ridgetheme + scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + theme(legend.key.size = unit(1,"cm")) + guides(colour = guide_legend(override.aes = list(size=10)))) 

(ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = LUM, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10))))


df <- FetchData(ds2_PA,vars = c("FB_score1","SMC_score1","BGN","LUM","UMAP_1","UMAP_2"))
# df <- arrange(df,FB_score1,by_group = F)
data <- cbind(df,index = 1:nrow(df),cluster = Idents(ds2_PA))

ggplot(data,aes(x=SMC_score1)) + geom_point(aes(y = BGN, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + theme_classic() + ridgetheme + scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10)))

ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = LUM, color = cluster),alpha = 1) + geom_smooth(aes(y = BGN), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5)) + scale_color_manual(values = colors_list) + guides(colour = guide_legend(override.aes = list(size=10)))

# ggplot(data,aes(x=FB_score1)) + geom_point(aes(y = BGN),color = "#e2b398",alpha = 1) + geom_smooth(aes(y = BGN), color = "red") + geom_point(aes(y=LUM),color = "#d1eba8",alpha = 1) + geom_smooth(aes(y = LUM), color = "green") + theme_classic() + ridgetheme +scale_y_continuous(limits = c(1,5))

---

fig.D

AC->PA

umapplot(ds2_PA,group.by = "ref_celltype", repel = T, label.size = 5) %>% 
  ggsave("./fig2/sup_ds2PA_umap2.png",plot = ., device = png, height = 4, width = 6)

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

numerical umap

embedding <- FetchData(object = ds2_PA, vars = c("UMAP_1", "UMAP_2"))
embedding <- cbind(embedding, cbind(ds2_PA$X0,ds2_PA$X1,ds2_PA$X2,ds2_PA$X3))

ggobj <- ggplot() +
  geom_point(data = embedding[embedding$`1`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `1`), shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('0', low = "#FFFFFF00", high = "#6dc0a6") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`2`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `2`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('1', low = "#FFFFFF00", high = "#e2b398") +
   new_scale("color") +
    geom_point(data = embedding[embedding$`3`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `3`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('2', low = "#FFFFFF00", high = "#e2a2ca") +
  new_scale("color") +
    geom_point(data = embedding[embedding$`4`>0.1,], 
             aes(x = UMAP_1, y = UMAP_2, color = `4`),shape=16, size = 3, alpha=0.5) + 
  scale_color_gradient('3', low = "#FFFFFF00", high = "#d1eba8") +
        xlab("UMAP 1") + ylab("UMAP 2")  +
        theme(axis.line = element_line(arrow = arrow(length = unit(0.2, "cm")))) +
        scale_y_continuous(breaks = NULL) +
        scale_x_continuous(breaks = NULL) + 
  theme(panel.background = element_blank(), panel.grid = element_blank(), legend.position = "bottom")
ggsave("./fig2/sup_ds2PA_umap.png",device = png, plot = ggobj, height = 10,width = 10)

PA->AC

train on PA

numerical umap

ggsave("./fig2/sup_ds2AC_umap.png",device = png, plot = ggobj,height = 10,width = 10)

Error in FUN(X[[i]], ...) : 找不到对象'0'

GSVA

GSVAres <- readRDS("GSVAres.rds")
es <- data.frame(t(GSVAres),stringsAsFactors=F)  #可视化相关通路的在umap上聚集情况
ds2_AC <- AddMetaData(ds2_AC, es)
f("CUI_TCF21_TARGETS_UP", label = F, ds2_AC) +scale_colour_gradient(low="#1E90FF", high="#ff2121")

ds2_PA <- AddMetaData(ds2_PA, es)
f("CUI_TCF21_TARGETS_UP", label = F, ds2_PA) +scale_colour_gradient(low="#1E90FF", high="#ff2121")

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